






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :str  ,     lowerCamelCase__     :Dict     ):
					if isinstance(lowerCamelCase__  ,     lowerCamelCase__     ):
						# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
						# modified it, it will not be accepted here again, since `auto` values would have been overridden
						UpperCamelCase__ :Optional[Any]						=      deepcopy(lowerCamelCase__     )
					elif os.path.exists(lowerCamelCase__     ):
						with io.open(lowerCamelCase__  ,     """r"""  ,     encoding="""utf-8"""     ) as f:
							UpperCamelCase__ :int						=      json.load(lowerCamelCase__     )
					else:
						try:
							UpperCamelCase__ :int						=      baseaa.urlsafe_baadecode(lowerCamelCase__     ).decode("""utf-8"""     )
							UpperCamelCase__ :Union[str, Any]						=      json.loads(lowerCamelCase__     )
						except (UnicodeDecodeError, AttributeError, ValueError):
							raise ValueError(
							    f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""     )

					UpperCamelCase__ :Tuple						=      config

					self.set_stage_and_offload()







				def 				__a   ( self     :Tuple     ):
					# zero stage - this is done as early as possible, before model is created, to allow
					# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
					# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
					UpperCamelCase__ :Dict						=      self.get_value("""zero_optimization.stage"""  ,     -1     )

					# offload
					UpperCamelCase__ :Tuple						=      False
					if self.is_zeroa() or self.is_zeroa():
						UpperCamelCase__ :Optional[int]						=      set(["""cpu""", """nvme"""]     )
						UpperCamelCase__ :Optional[Any]						=      set(
						    [
						        self.get_value("""zero_optimization.offload_optimizer.device"""     ),
						        self.get_value("""zero_optimization.offload_param.device"""     ),
						    ]     )
						if len(offload_devices & offload_devices_valid     ) > 0:
							UpperCamelCase__ :Optional[int]						=      True







				def 				__a   ( self     :Union[str, Any]  ,     lowerCamelCase__     :str     ):
					UpperCamelCase__ :Tuple						=      self.config

					# find the config node of interest if it exists
					UpperCamelCase__ :Any						=      ds_key_long.split("""."""     )
					UpperCamelCase__ :int						=      nodes.pop()
					for node in nodes:
						UpperCamelCase__ :Dict						=      config.get(lowerCamelCase__     )
						if config is None:
							return None, ds_key

					return config, ds_key







				def 				__a   ( self     :Dict  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Optional[Any]=None     ):
					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      self.find_config_node(lowerCamelCase__     )
					if config is None:
						return default
					return config.get(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Tuple  ,     lowerCamelCase__     :str  ,     lowerCamelCase__     :Tuple=False     ):
					UpperCamelCase__ :Tuple						=      self.config

					# find the config node of interest if it exists
					UpperCamelCase__ :Optional[Any]						=      ds_key_long.split("""."""     )
					for node in nodes:
						UpperCamelCase__ :Any						=      config
						UpperCamelCase__ :Optional[Any]						=      config.get(lowerCamelCase__     )
						if config is None:
							if must_exist:
								raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""     )
							else:
								return

        # if found remove it
					if parent_config is not None:
						parent_config.pop(lowerCamelCase__     )







				def 				__a   ( self     :Any  ,     lowerCamelCase__     :str     ):
					UpperCamelCase__ :Any						=      self.get_value(lowerCamelCase__     )
					return False if value is None else bool(lowerCamelCase__     )







				def 				__a   ( self     :List[Any]  ,     lowerCamelCase__     :List[Any]     ):
					UpperCamelCase__ :Dict						=      self.get_value(lowerCamelCase__     )
					return False if value is None else not bool(lowerCamelCase__     )







				def 				__a   ( self     :Dict     ):
					return self._stage == 2







				def 				__a   ( self     :List[str]     ):
					return self._stage == 3







				def 				__a   ( self     :Optional[int]     ):
					return self._offload

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Union[str, Any]  ,     lowerCamelCase__     :int     ):
					UpperCamelCase__ :Union[str, Any]						=      engine







				def 				__a   ( self     :str  ,     lowerCamelCase__     :List[str]  ,     **lowerCamelCase__     :Optional[int]     ):
					# runs backpropagation and handles mixed precision
					self.engine.backward(lowerCamelCase__  ,     **lowerCamelCase__     )

					# Deepspeed's `engine.step` performs the following operations:
					# - gradient accumulation check
					# - gradient clipping
					# - optimizer step
					# - zero grad
					# - checking overflow
					# - lr_scheduler step (only if engine.lr_scheduler is not None)
					self.engine.step()
					# and this plugin overrides the above calls with no-ops when Accelerate runs under
					# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
					# training loop that works transparently under many training regimes.

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def __init__( self     :Dict  ,     lowerCamelCase__     :Tuple     ):
					super().__init__(lowerCamelCase__  ,     device_placement=lowerCamelCase__  ,     scaler=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      hasattr(self.optimizer  ,     """overflow"""     )







				def 				__a   ( self     :int  ,     lowerCamelCase__     :str=None     ):
					pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







				def 				__a   ( self     :List[Any]     ):
					pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







				@property
				def 				__a   ( self     :List[Any]     ):
					if self.__has_overflow__:
						return self.optimizer.overflow
					return False

class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				def __init__( self     :List[Any]  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Tuple     ):
					super().__init__(lowerCamelCase__  ,     lowerCamelCase__     )







				def 				__a   ( self     :Union[str, Any]     ):
					pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Optional[Any]  ,     lowerCamelCase__     :List[str]  ,     lowerCamelCase__     :Dict=0.001  ,     lowerCamelCase__     :Optional[int]=0  ,     **lowerCamelCase__     :List[Any]     ):
					UpperCamelCase__ :Any						=      params
					UpperCamelCase__ :Tuple						=      lr
					UpperCamelCase__ :Optional[int]						=      weight_decay
					UpperCamelCase__ :Optional[Any]						=      kwargs

class        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Any  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :int=None  ,     lowerCamelCase__     :List[str]=0  ,     **lowerCamelCase__     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      optimizer
					UpperCamelCase__ :List[Any]						=      total_num_steps
					UpperCamelCase__ :Any						=      warmup_num_steps
					UpperCamelCase__ :Any						=      kwargs




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  Optional[Any]			     =			{
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			['MvpTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MvpForCausalLM',
	    'MvpForConditionalGeneration',
	    'MvpForQuestionAnswering',
	    'MvpForSequenceClassification',
	    'MvpModel',
	    'MvpPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	a      :  Optional[Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






